class_docstr: WaveNet estimator that uses the architecture proposed in [Oord2016]_ with quantized targets.
  
  The model is based on a CNN architecture with dilated convolutions. Time series values are quantized into buckets
  and the model is trained using the cross-entropy loss.
  
  Based on `gluonts.torch.model.wavenet.WaveNetEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.wavenet.html>`_.
  See GluonTS documentation for additional hyperparameters.
  
  References
  ----------
  .. [Oord2016] Oord, Aaron van den, et al.
      "Wavenet: A generative model for raw audio"
      arXiv preprint arXiv:1609.03499. 2016.
  
  Other Parameters
  ----------------
  num_bins : int, default = 1024
      Number of bins used for quantization of the time series.
  num_residual_channels : int, default = 24
      Number of residual channels in WaveNet architecture.
  num_skip_channels : int, default = 32
      Number of skip channels in WaveNet architecture, by default 32
  dilation_depth : int or None, default = None
      Number of dilation layers in WaveNet architecture. If set to None (default), dilation_depth is set such that
      the receptive length is at least as long as the ``seasonality`` and at least ``2 * prediction_length``.
  num_stacks : int, default = 1
      Number of dilation stacks in WaveNet architecture.
  temperature : float, default = 1.0
      Temperature used for sampling from softmax distribution.
  seasonality : int, optional
      The seasonality of the time series. By default is set based on the ``freq`` of the data.
  embedding_dimension : int, default = 5
      The dimension of the embeddings for categorical features.
  use_log_scale_feature : bool, default = True
      If True, logarithm of the scale of the past data will be used as an additional static feature.
  negative_data : bool, default = True
      Flag indicating whether the time series take negative values.
  max_epochs : int, default = 100
      Number of epochs the model will be trained for
  batch_size : int, default = 64
      Size of batches used during training
  predict_batch_size : int, default = 500
      Size of batches used during prediction.
  num_batches_per_epoch : int, default = 50
      Number of batches processed every epoch
  lr : float, default = 1e-3,
      Learning rate used during training
  trainer_kwargs : dict, optional
      Optional keyword arguments passed to ``lightning.Trainer``.
  early_stopping_patience : int or None, default = 20
      Early stop training if the validation loss doesn't improve for this many epochs.
  weight_decay : float, default = 1e-8
      Weight decay regularization parameter.
code: def _get_estimator_class(self) -> Type[GluonTSEstimator]:
      from gluonts.torch.model.wavenet import WaveNetEstimator
  
      return WaveNetEstimator
  
imports: import logging
  from typing import Any, Dict, Type
  from gluonts.model.estimator import Estimator
  from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
label_desc: Use image node_id 1 for calling the WaveNetModel obj's underlying member method code with example usage: obj._get_estimator_class() and returns: WaveNetEstimator